Streamlining workflows with pipelines  Loading the Breast Cancer Wisconsin dataset  Combining transformers and estimators in a pipelineUsing k-fold cross-validation to assess model performance  The holdout method  K-fold cross-validationDebugging algorithms with learning and validation curves  Diagnosing bias and variance problems with learning curves  Addressing overfitting and underfitting with validation curvesFine-tuning machine learning models via grid search  Tuning hyperparameters via grid search  Algorithm selection with nested cross-validationLooking at different performance evaluation metrics  Reading a confusion matrix
  Optimizing the precision and recall of a classification model
  Plotting a receiver operating characteristic  The scoring metrics for multiclass classification
Summary